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In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, forcommunication-efficiency distributed Deep Neural Network (DNN) training.$\mathsf{CO}_3$ is a joint training/communication protocol, which encompassesthree processing steps for the network gradients: (i) quantization throughfloating-point conversion, (ii) lossless compression, and (iii) errorcorrection. These three components are crucial in the implementation ofdistributed DNN training over rate-constrained links. The interplay of thesethree steps in processing the DNN gradients is carefully balanced to yield arobust and high-performance scheme. The performance of the proposed scheme isinvestigated through numerical evaluations over CIFAR-10.

Convert, compress, correct: Three steps toward communication-efficient DNN training